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The purpose of this study is to compare the predictive performance of the hedonic multivariate regression model with the probabilistic neural network (PNN)-based general regression neural network (GRNN) model of housing prices in “Pune-India.”

Data on 211 properties across “Pune city-India” is collected. The price per square feet is considered as a dependent variable whereas distances from important landmarks such as railway station, fort, university, airport, hospital, temple, parks, solid waste site and stadium are considered as independent variables along with a dummy for amenities. The data is analyzed using a hedonic type multivariate regression model and GRNN. The GRNN divides the entire data set into two sets, namely, training set and testing set and establishes a functional relationship between the dependent and target variables based on the probability density function of the training data (Alomair and Garrouch, 2016).

While comparing the performance of the hedonic multivariate regression model and PNN-based GRNN, the study finds that the output variable (i.e. price) has been accurately predicted by the GRNN model. All the 42 observations of the testing set are correctly classified giving an accuracy rate of 100%. According to Cortez (2015), a value close to 100% indicates that the model can correctly classify the test data set. Further, the root mean square error (RMSE) value for the final testing for the GRNN model is 0.089 compared to 0.146 for the hedonic multivariate regression model. A lesser value of RMSE indicates that the model contains smaller errors and is a better fit. Therefore, it is concluded that GRNN is a better model to predict the housing price functions. The distance from the solid waste site has the highest degree of variable senstivity impact on the housing prices (22.59%) followed by distance from university (17.78%) and fort (17.73%).

The study being a “case” is restricted to a particular geographic location hence, the findings of the study cannot be generalized. Further, as the objective of the study is restricted to just to compare the predictive performance of two models, it is felt appropriate to restrict the scope of work by focusing only on “location specific hedonic factors,” as determinants of housing prices.

The study opens up a new dimension for scholars working in the field of housing prices/valuation. Authors do not rule out the use of traditional statistical techniques such as ordinary least square regression but strongly recommend that it is high time scholars use advanced statistical methods to develop the domain. The application of GRNN, artificial intelligence or other techniques such as auto regressive integrated moving average and vector auto regression modeling helps analyze the data in a much more sophisticated manner and help come up with more robust and conclusive evidence.

To the best of the author’s knowledge, it is the first case study that compares the predictive performance of the hedonic multivariate regression model with the PNN-based GRNN model for housing prices in India.

This paper examines whether the successful bid rate of the OnBid public auction, published by Korea Asset Management Corporation, can identify and forecast the Korea business-cycle expansion and contraction regimes characterized by the OECD reference turning points. We use logistic regression and support vector machine in performing the OECD regime classification and predicting three-month-ahead regime. We find that the OnBid auction rate conveys important information for detecting the coincident and future regimes because this information might be closely related to deleveraging regarding default on debt obligations. This finding suggests that corporate managers and investors could use the auction information to gauge the regime position in their decision-making. This research has an academic significance that reveals the relationship between the auction market and the business-cycle regimes.

Internationalization of capital markets gives opportunities to investors to invest their money in the country of their choice, not just in their own country. The relationships between international stock markets have become increasingly important in recent times. The purpose of this paper is to study the inter‐linkages between stock markets of India, Pakistan and Sri Lanka.

This paper studies the inter‐linkages between stock markets of India, Pakistan and Sri Lanka. Daily closing levels of the benchmark indices in the three countries are taken for a period of January 2003‐June 2010. While line charts, correlogram and unit‐root test are applied to check the stationary nature of the series; Granger's causality model, vector auto regression (VAR) model and variance decomposition analysis are performed to find out the linkages between the markets under study.

The paper concludes that while the National Stock Exchange (India) Granger causes Karachi Stock Exchange (Pakistan) and Colombo Stock Exchange (Sri Lanka), the vice versa is not true. These results of Granger's causality model are also confirmed by the VAR models.

Studies have been conducted in large numbers to test the linkages and integration between stock exchanges of the developed nations, namely the USA, Canada, Europe and Japan. Even the studies that have focused on the developing and under‐developed nations have studied the linkages of those with the developed nations. Little research has been conducted about the inter‐linkages between the nations from Asia. Even fewer studies have focused on stock exchanges in the South‐Asian region. This research paper focuses on the return from the benchmark stock exchanges from these three countries and also on the linkages between India, Pakistan and Sri Lanka.

This study aims to explain how delinquency shocks in one type of debt contaminate the others. That is, the authors aim to shed light on the time pattern of delinquencies in different debt types.

This study analyzes the interdependencies between mortgage, credit card and auto loans delinquency rates in the USA from 2003 to 2019, using a panel VAR-X, the panel Granger causality tests and the Geweke linear dependence measures. The authors also compute the impulse response functions of a shock to one kind of debt on the others and decompose the variance of the forecast errors.

The authors find a statistically significant bidirectional Granger causality between the delinquencies. The Geweke measures of linear dependence and the Dumitrescu and Hurlin Granger non-causality tests support that mortgage predominantly causes credit card and auto loan delinquencies. Auto loans also cause credit card delinquencies. The impulse response functions confirm this pattern. This scenario aligns with a sequence where debtors consider rational first to default on credit cards, second on auto loans and only on mortgages in the last instance. Indeed, credit card delinquencies Granger-cause delinquencies in other debts when it occurs.

To the best of the authors’ knowledge, this is the first study to focus on the temporal pattern of delinquency rates for all the US states, using panel data. Furthermore, the results call for policymakers to design regulations to break the transmission channel from debt delinquencies.

The purpose of this paper is to empirically investigate the factors deriving effective tax rate (ETR) for Indian manufacturing firms in different sectors. The study also tries to analyze the sensitiveness of ETR because of shocks on its key determinants.

The study is using Arellano–Bond dynamic panel regression model to identify the key drivers of ETR, and impulse response functions of panel vector auto-regression model to analyze the response of ETR because of one standard deviation (SD) shock to its key determinants.

This study concludes that ETR is significantly explained by firm size, profitability, growth rate and non-debt tax shield in most of the sectors, and debt ratio, asset tangibility and age of the firms are impacting ETR differently across sectors. In case of entire manufacturing sector, firm size, profitability, growth and non-debt tax shield are driving ETR positively and asset tangibility is driving ETR negatively. Interest coverage ratio (ICR) and firm age are not significant drivers of ETR. ETR is positively related with firm size, but responses negatively when there is an immediate shock to firm size. Similarly, ETR is negatively related with asset tangibility, but responds positively following an immediate shock to it. Overall, ETR is more sensitive and responses significantly because of shocks in firm size, profitability, growth, asset tangibility and non-debt tax shield whereas, the response is very marginal following shocks to debt ratio, ICR and age of the firm.

Firm managers may find the study useful to understand the receptiveness of ETRs at each sector level. The empirical findings are not only validating the theoretical developments but also providing a root cause analysis to the firm managers to understand the cause and consequence of ETRs for firms at different sectors.

Empirically investigating the factors driving ETR and analyzing its sensitiveness because of one SD shock on its key determinants for Indian manufacturing firms from different sectors is the originality of this study. Developing a strong theoretical background and empirically validating it through advanced methodology makes the study unique.

Property market models have the overriding aim of predicting reasonable estimates of key dependent variables (demand, supply, rent, yield, vacancy and net absorption rate). These can be based on independent drivers of core property and economic activities. Accurate predictions can only be conducted when ample quantitative data are available with fewer uncertainties. However, a broad-fronted social, technical and ecological evolution can throw up sudden, unexpected shocks that result in the econometric outputs sceptical to unknown risk factors. Therefore, the purpose of this paper is to evaluate Australian office market forecast accuracy and to determine whether the forecasts capture extreme downside risk events.

This study follows a quantitative research approach, using secondary data analysis to test the accuracy of economists’ forecasts. The forecast accuracy evaluation encompasses the measurement of economic and property forecasts under the following phases: testing for the forecast accuracy; analysing outliers of forecast errors; and testing of causal relationships. Forecast accuracy measurement incorporates scale independent metrics that include Theil’s U values (U1 and U2) and mean absolute scaled error. Inter-quartile range rule is used for the outlier analysis. To find the causal relationships among variables, the time series regression methodology is utilised, including multiple regression analysis and Granger causality developed under the vector auto regression (VAR).

The credibility of economic and property forecasts was questionable around the period of the Global Financial Crisis (GFC); a significant man-made Black Swan event. The forecast accuracy measurement highlighted rental movement and net absorption forecast errors as the critical inaccurate predictions. These key property variables are explained by historic information and independent economic variables. However, these do not explain the changes when error time series of the variables were concerned. According to VAR estimates, all property variables have a significant causality derived from the lagged values of Australian S&P/ASX 200 (ASX) forecast errors. Therefore, lagged ASX forecast errors could be used as a warning signal to adjust property forecasts.

Secondary data were obtained from the premier Australian property markets: Canberra, Sydney, Brisbane, Adelaide, Melbourne and Perth. A limited ten-year timeframe (2001-2011) was used in the ex-post analysis for the comparison of economic and property variables. Forecasts ceased from 2011, due to the discontinuity of the Australian Financial Review quarterly survey of economists; the main source of economic forecast data.

The research strongly recommended naïve forecasts for the property variables, as an input determinant in each office market forecast equation. Further, lagged forecast errors in the ASX could be used as a warning signal for the successive property forecast errors. Hence, data adjustments can be made to ensure the accuracy of the Australian office market forecasts.

The paper highlights the critical inaccuracy of the Australian office market forecasts around the GFC. In an environment of increasing incidence of unknown events, these types of risk events should not be dismissed as statistical outliers in real estate modelling. As a proactive strategy to improve office market forecasts, lagged ASX forecast errors could be used as a warning signal. This causality was mirrored in rental movements and total vacancy forecast errors. The close interdependency between rents and vacancy rates in the forecasting process and the volatility in rental cash flows reflects on direct property investment and subsequently on the ASX, is therefore justified.

Deficits in fiscal and current account balances in a large number of countries reveal interesting implications of the causal relationship between internal and external imbalances. Empirical evidence about the occurrence of so-called twin deficits or twin surpluses provides crucial information about the validity of an intertemporal approach. However, most recent dynamic cyclical changes during the crisis period revealed many questions about the direct interconnection between macroeconomic performance and twin imbalances. In the paper we observe substantial features of twin imbalances in European transition economies. Event study (identification of large fiscal and current account changes and their parallel occurrence) and vector auto-regression methods will be employed to examine key aspects of twin imbalances. Our results suggest that current account deteriorations were predominately associated with negative public investment and savings balances (fiscal deficits), while current account improvements were predominately associated with positive private investment and savings balances, confirming empirical evidence about twin deficits in European transition economies.

The purpose of this paper is to investigate the dynamic relationship among Gold, Crude oil, Indian Rupee-US Dollar and Stock market-Sensex (gold, oil, dollar and stock market (GODS)) in the pre-crisis, the crisis and the post-crisis periods in the Indian context.

The authors use Johansen’s cointegration technique, Vector Error Correction Model (VECM), Vector Auto Regression, VEC Granger Causality/Block Exogeneity Wald Test, and Granger Causality and Toda Yamamoto modified Granger causality to study long-run relationship and causality.

Johansen’s cointegration test results indicate that there is a long-run equilibrium relationship among the variables in the pre-crisis and the crisis periods but not in post-crisis period. VECM results report that none of four models of the variables show long-run causality in the pre-crisis period. During the crisis period, both crude oil and Sensex models show long-run causality. However, in some cases, results indicate short-run causality. The authors find one-way causality from USD and Sensex to crude oil, and from gold and Sensex to USD. Thus, the authors conclude that the relationship among GODS is dynamic across global financial crisis.

The research findings of this study are vital to the large group of stakeholders and participants of gold, crude oil, US dollar and stock market in emerging economies like India. The results are useful to importers, exporters, government, policy makers, corporate houses, retail investors, portfolio managers, commodity traders, treasury and fund managers, other commercial traders, etc.

This study is one of its kinds as it investigates the relationship among GODS in India in different sub-periods like before, during and after the global financial crisis of 2008. None of the studies compare phase-wise relationship among GODS in the Indian context. The study contributes to the economic theory and the body of knowledge. It highlights the need to revisit the economic theory to explain the interplay mechanism among GODS.

The purpose of this paper is to gain insights useful to explain the loan puzzle: the unexpected increase of loans to firms in case of a monetary tightening. To this end, the authors develop the analysis using several loan categories distinguished by lender, scope and borrower. This approach helps to unveil significant differences on how those categories respond to the same shock and allow to evaluate possible alternative explanations for such differences.

The paper is empirical. The analysis is based on a large vector auto-regression, estimated using Bayesian techniques and has as object the US economy.

The findings support a supply-side explanation of the loan puzzle, i.e. banks reshuffle their portfolio in favor of short-term business loans after a monetary tightening. Moreover, the authors achieve the following results. First, the analysis shows that loans to small firms increase as well, but less than what observed with large firms: small firms stay between large firms and households. Second, considering advances and other loans allows to conclude that finance companies behave very much as banks. Third, some limited evidence suggests that not just industrial and commercial loans to firms might increase but also more long-term loans, such as mortgages.

The authors develop an analysis, based on state-of-the-art Bayesian techniques, that reveals the differential response of well-distinguished loan categories to several shocks; monetary and real shocks in the first place. After showing their heterogenous response, the authors discuss it in detail, with specific reference to supply and demand factors of credit intrinsic to the transmission mechanism. With respect to previous contributions, the authors consider a plurality of loan categories functional to understand the reason behind each specific response. This allows to conclude in favor of supply factors as an explanation of the unexpected increase of loans to corporate firms in case of a monetary shock.

The purpose of this paper is to demonstrate importance of usage of sector indices which provides insight for sector specific investment strategies and direction for suitable policy formulation for the Indian industry. It investigates long run, short run and causality relationships between eight identified sector indices and Sensex for the post subprime period.

The study uses Vector Error Correction Model (VECM) for econometric analysis. It employs Generalized Impulse Response and Variance Decomposition analysis for developed multivariate framework in order to provide information about precise interplay of the sector indices.

Long-term relationships between sector indices were determined by the usage of VECM indicating minimal benefits from diversifying investments to different sectors. Limited lead – lag short run relationships between sector indices were observed. Banking index played a predominant and integrating role in moving other indices. During this period of recovery; most sectors were protected and provided marginally better returns due to robust Banking policy. Realty and Metal were other significant drivers influencing remaining sectors contemporaneously. The study for the post subprime crisis period helps to understand the importance and behavior of interrelated sector indices and Sensex in the dynamic economic environment.

The study clearly provides direction for sector specific investment strategies and policy formulation.

The study highlights utility and importance of usage of sector indices. No study using sector indices for the Indian economy have been done earlier employing VAR for the post subprime crisis period.